The value of Big Data: How analytics
differentiates winners
In our survey, only 4% of companies said they had the
right people, tools, data and intent to draw meaningful
insights from that data—and to act on them.
By Rasmus Wegener and Velu Sinha
Rasmus Wegener is a partner with Bain & Company in Atlanta, and
Velu Sinha is a Bain partner in Silicon Valley. Both work with the firm’s
Global Technology practice.
The authors would like to acknowledge the contributions of James Dillard,
a consultant with Bain & Company in Atlanta.
Copyright © 2013 Bain & Company, Inc. All rights reserved.
The value of Big Data: How analytics differentiates winners
Big Data is quickly becoming a critically important driver
of business success across sectors, but many executives
say they don’t think their companies are equipped to
make the most of it. Bain & Company surveyed executives
at more than 400 companies around the world, most
with revenues of more than $1 billion. We asked them
about their data and analytics capabilities and about their
decision-making speed and effectiveness.
As we describe in a companion brief, “Big Data: The
organizational challenge,” achieving competency in
Big Data is a three-part process that requires setting
the ambition, building up the analytics capability and
organizing your company to make the most of the
opportunity. This brief looks more closely at the second
step—building up the analytics capability—to see how
leaders use Big Data to get ahead.
The results were surprising: We found that only 4% of
companies are really good at analytics, an elite group that
puts into play the right people, tools, data and intentional
focus. These are the companies that are already using
analytics insights to change the way they operate or to
improve their products and services. And the difference
is already visible. These companies are:
Data, tools, people and intent
•
Twice as likely to be in the top quartile of financial
performance within their industries
Leaders build up their analytics capabilities by investing
in four things: data-savvy people, quality data, state-ofthe-art tools, and processes and incentives that support
analytical decision making (see Figure 1). About a third
of companies don’t do any of these well, and many of the
rest excel in only one or two areas. But to build a highperforming analytics machine, you need to do all four well.
Success in each capability depends on strength in the others.
•
Three times more likely to execute decisions as intended
•
•
Five times more likely to make decisions faster
Data. Companies need a strategic plan for collecting and organizing data, one that aligns with the
business strategy of how they will use that data to
Figure 1: Four elements are important for developing advanced analytical capabilities
Mix of data science, business
acumen and technical expertise
Resolving to be data driven;
creating structures, processes
and incentives to support
analytical decision making
• 36% of companies surveyed have
a dedicated data-insights team
Quality, consistent data stored in
a manner that is easy to access
People
Intent
Data
Tools
• Only 19% of companies surveyed
have “high quality, consistent data”
in their organizations
• 23% of companies surveyed
have clear strategies for using
analytics effectively
State-of-the-art tools like
unstructured databases,
scale-out compute clusters and
heuristic instrumentation
• 38% of companies surveyed
are using state-of-the-art
analytics tools
Source: Bain research, n=409
1
The value of Big Data: How analytics differentiates winners
create value. In our analytics survey, 56% of the
companies didn’t have the right systems to capture
the data they needed or weren’t collecting useful
data, and 66% lacked the right technology to store
and access data. A good data policy identifies relevant
data sources and builds a data view on the business
in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good
data policy is to focus on identifying relevant sources
of data. For example, capturing all queries made on
the company website or from customer support calls,
emails or chat lines, regardless of their outcome,
may have significant value in identifying emerging
trends; however, keeping detailed logs of requests
that were easily handled might be less valuable.
•
Leading companies embed analytics into
their organizations by resolving to be
data driven and defining what they hope
to accomplish through their use of Big
Data. The CEO and top leadership team
need to describe how analytics will shape
the business’s performance.
•
Tools. Aim high in your aspirations of what’s possible. Advanced analytics and Big Data tools are
developing so rapidly that they’re likely to help you
get to potential insights and statistical novelties in
ways that were not possible even as recently as a
year ago. Tools and platforms like Hadoop, HPCC
and NoSQL are rapidly emerging and evolving to
address analytics opportunities, as is the rich ecosystem of mature analytics, visualization and data
management. Today, these tools are available from a
wide range of vendors and an even larger community
of open-source developers. Still, and somewhat
surprising, in our survey, only 38% of companies
said they were using any of these.
People. In our survey, 56% of executives said their
companies lacked the capabilities to develop deep,
data-driven insights. Most agreed they were not up
to the challenges of identifying and prioritizing
what types of insights would be most relevant to the
business. Successful analytics teams build those
capabilities by blending data, technical and business
talent. Think of a band as the model: a team with
different but overlapping skills that knows how to
effectively and efficiently communicate and collaborate. Successful Big Data and analytics efforts need:
––
Data scientists, who provide expertise in statistics,
correlations and quality;
––
Business analysts, who identify and prioritize
the problems worth solving and the business
relevance of data anomalies and patterns identified by the data scientists;
––
Technical specialists, who help manage the
hardware and software solutions needed to
collect, clean and process the data;
––
Organizational intent. Leading companies
embed analytics into their organizations by
resolving to be data driven and defining what
they hope to accomplish through their use of
Big Data. The CEO and top leadership team
need to describe how analytics will shape the
business’s performance, whether by improving
existing products and services, optimizing
internal processes, building new products or
service offerings, or transforming business
models. Top-performing organizations do this
well, often building their organizations around
data and a commitment to make data-driven
decisions (see Figure 2).
Nest is a good example of a company that built into
its business model the intent to learn from advanced
analytics and Big Data. Other companies offer their
customers the ability to remotely control their home
thermostats through a Web interface or their smart-
2
The value of Big Data: How analytics differentiates winners
Figure 2: Top performers embed data in their decision making
Percentage of respondents by data capabilities
60%
53%
47%
41%
40
Top
performer
28%
20
15%
Everyone
else
6%
0
Make data-driven decisions
“very frequently”
Make decisions “much faster”
than market peers
Execute decisions as intended
“most of the time”
Source: Bain research, n=409
The opportunity, the urgency
phones. Nest goes further, crowdsourcing intelligence
about when and how customers adjust their thermostats to keep their homes comfortable. Nest gathers
that information in the cloud, and by correlating
it with weather, location, type of home and when
people adjust their thermostats, the company can
anticipate and control the settings to create a more
comfortable environment in their customers’ homes.
The opportunity to deploy advanced analytics to outperform the competition is real, and top-performing
companies see themselves as more effective in every
aspect of analytics, including capturing, collecting and
storing data, as well as parsing and drawing insights
from it (see Figure 3).
Some industries are farther along than others—financial
services, technology and healthcare, for example, are
leading players in redefining the battlegrounds and
business models, based on their analytics capabilities
and insight-driven decisions. But opportunities exist in
almost every industry. Consider the mail-order pharmacy
that analyzed hundreds of thousands of customer service
logs and detected a spike in calls between Days 75 and
105 of some patients’ medication regimens. Looking
closer, analysts found that the calls correlated with refill
dates, and they discovered that some customers were
Committing to excellence in each of these four categories
can require dramatic changes, significant investment
and occasionally a change in leadership. But it’s no good
focusing on one of these four areas without the other
three. Tools won’t help if the data is of poor quality,
and talent will walk if the company isn’t committed to
benefiting from the insights. Like an engine that must
be firing on all pistons, all four areas must be tuned
for peak performance.
3
The value of Big Data: How analytics differentiates winners
Figure 3: Leaders are more effective at the tactical aspects of analytics across every category
Which of the following are reasons why your organization is not more effective at generating data-driven insights?
100%
Top performers: 0%
80
60
40
20
0
We lack systems
to capture data
Useful data isn’t
collected
Laggards
We lack
technology to
store and
access data
Data isn’t stored
in a useful form
Low
Medium
High
We lack
capabilities to
run databases/
other data tech
We lack the
capabilities to
develop datadriven insights
Top performers
Source: Bain research, n=409
calling for refills because their medications were taken
with variable dosages. To reduce the number of lengthy
customer service calls and expensive “emergency” refills
and rush orders, the pharmacy began asking patients
how many pills they had remaining at Day 30 and Day
60, so that they could better predict when the medication would run out.
could answer the phone with a pretty good idea of why
you’re calling. More in-depth analysis could correlate
your ID with your social media presence. If you’ve just
tweeted an irate message about being booted from a flight,
the rep answering your call may have already read it.
More sophisticated still, new technologies like sentiment
analysis can use pattern recognition to detect a caller’s
mood at the start of a call. An exasperated caller might be
quickly routed to a specialist in kid-glove management.
And within any industry, some functions can benefit
from insights gleaned through Big Data analytics. Call
centers, for instance, can be made more effective and
efficient by capitalizing on what the company can know
about the caller ahead of time. And airlines have for years
been able to route premium-status fliers to higher-level
customer service representatives by recognizing their
caller IDs. Now they can do even more: By making a quick
correlation between your ID, your booked flights and
the status of those flights, they may be able to determine
why you’re calling, even before the second ring. If your
next flight has just been delayed, the representative
The competitive edge to be gained from advanced
analytics is no longer limited to a few techy companies
or data-intensive industries. It’s here today, in all sectors,
and as our survey results demonstrate, companies that
commit to making the most of their data and investing
in their analytics capabilities are already outperforming
their peers financially. A wait-and-see attitude is a luxury
that no competitive company can afford.
4
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Key contacts in Bain’s Technology and Organization practices:
Americas:
Rasmus Wegener in Atlanta (rasmus.wegener@bain.com)
Eric Almquist in Boston (eric.almquist@bain.com)
Velu Sinha in Palo Alto (velu.sinha@bain.com)
Chris Brahm in San Francisco (chris.brahm@bain.com)
Travis Pearson in San Francisco (travis.pearson@bain.com)
Europe,
Middle East
and Africa:
Stephen Bertrand in London (stephen.bertrand@bain.com)
Lars Jacob Boe in Oslo (larsjacob.boe@bain.com)
Laurent-Pierre Baculard in Paris (laurent-pierre.baculard@bain.com)
Asia-Pacific:
Chris Harrop in Melbourne (chris.harrop@bain.com)
Arpan Sheth in Mumbai (arpan.sheth@bain.com)
Moonsup Shin in Seoul (moonsup.shin@bain.com)
For more information, visit www.bain.com